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Environmental Diagnostics, a thematic research programme of the UK Natural Environment Research Council, set out in 1995 to provide a better scientific basis for the sustainable management of chemicals and to provide industry and government with tools that would help manage their manufacture, use and disposal.

EDIE First BodyEnvironmental Diagnostics (ED) began in 1995 and is drawing to a close. As well as funding from the NERC of some £6.5m, the programme attracted around £2m in support from government departments and agencies and through 'in kind' and 'parallel' contributions from industry and other users, such as environmental consultants.

Environmental Diagnostics includes studies in air, land, water and deep underground - where contamination poses a threat to the purity of aquifers. Environmental Diagnostics informs both the Source-Pathway-Receptor concept, and current perspectives on risk assessment and sustainability.

Some of the work has involved working closely with industry on real world situations, for example in helping predict the likely underground consequences of surface spills, and in handling the problems regulators face with mixtures of pollutants. Other projects have produced readily available tools, in the form of both field techniques and computer models that help to predict how water and pollutants move through environmental pathways. An improved conceptual understanding of the relative contributions of soil, sub-soil and groundwater processes to the breakdown of chemicals by natural attenuation has led to the development of better strategies for remediating land and managing chemical spills.

Opportunities for technology transfer, patents, improved risk management, frameworks for decision making, new national databases and maps, improved monitoring...The NERC ED programme has conducted many high quality, real-world observational campaigns and tested hypotheses in a wide range of ecosystems at various scales. The findings from such studies have been built into a series of predictive approaches and models that should considerably reduce the uncertainty in decision making.


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